lab10

Author

Yi Wang

Lab 10 - Interactive Visualization

We recommend downloading this QMD file to use as a template for your answers. You can download the file from: https://github.com/USCbiostats/PM566/blob/main/labs/lab9.qmd

We have set eval=FALSE as a global option.

To run a specific chunk, you can set eval=TRUE in that chunk.

To run all chunks, you can set eval=TRUE inside of opts_chunk$set() in the setup chunk.

Learning Goals

  • Read in and process the COVID dataset from the New York Times GitHub repository

  • Create interactive graphs of different types using plot_ly() and ggplotly() functions

  • Customize the hoverinfo and other plot features

  • Create a Choropleth map using plot_geo()

Lab Description

We will work with COVID data downloaded from the New York Times. The dataset consists of COVID-19 cases and deaths in each US state during the course of the COVID epidemic.

The objective of this lab is to explore relationships between cases, deaths, and population sizes of US states, and plot data to demonstrate this

Steps

I. Reading and processing the New York Times (NYT) state-level COVID-19 data

1. Read in the data

  • Read in the COVID data with read.csv() from the NYT GitHub repository: https://raw.githubusercontent.com/nytimes/covid-19-data/master/us-states.csv

  • Read in the state population data with read.csv() from the repository: https://raw.githubusercontent.com/COVID19Tracking/associated-data/master/us_census_data/us_census_2018_population_estimates_states.csv

  • Merge datasets

    ## data extracted from New York Times state-level data from NYT Github repository
# https://github.com/nytimes/covid-19-data

## state-level population information from us_census_data available on GitHub repository:
# https://github.com/COVID19Tracking/associated-data/tree/master/us_census_data

### FINISH THE CODE HERE ###
# load COVID state-level data from NYT
cv_states <- read.csv("https://raw.githubusercontent.com/nytimes/covid-19-data/master/us-states.csv")

### FINISH THE CODE HERE ###
# load state population data
state_pops <- read.csv("https://raw.githubusercontent.com/COVID19Tracking/associated-data/master/us_census_data/us_census_2018_population_estimates_states.csv")

# adjust column names
state_pops$abb <- state_pops$state
state_pops$state <- state_pops$state_name
state_pops$state_name <- NULL

### FINISH THE CODE HERE
cv_states <- merge(cv_states,state_pops, by="state")

2. Look at the data

  • Inspect the dimensions, head, and tail of the data

  • Inspect the structure of each variables

    dim(cv_states)
    [1] 58094     9
    head(cv_states)
        state       date fips   cases deaths geo_id population pop_density abb
1 Alabama 2023-01-04    1 1587224  21263      1    4887871    96.50939  AL
2 Alabama 2020-04-25    1    6213    213      1    4887871    96.50939  AL
3 Alabama 2023-02-26    1 1638348  21400      1    4887871    96.50939  AL
4 Alabama 2022-12-03    1 1549285  21129      1    4887871    96.50939  AL
5 Alabama 2020-05-06    1    8691    343      1    4887871    96.50939  AL
6 Alabama 2021-04-21    1  524367  10807      1    4887871    96.50939  AL
    tail(cv_states)
            state       date fips  cases deaths geo_id population pop_density abb
58089 Wyoming 2022-09-11   56 175290   1884     56     577737    5.950611  WY
58090 Wyoming 2022-08-21   56 173487   1871     56     577737    5.950611  WY
58091 Wyoming 2021-01-26   56  51152    596     56     577737    5.950611  WY
58092 Wyoming 2021-02-21   56  53795    662     56     577737    5.950611  WY
58093 Wyoming 2021-08-22   56  70671    809     56     577737    5.950611  WY
58094 Wyoming 2021-03-20   56  55581    693     56     577737    5.950611  WY
    str(cv_states)
    'data.frame':   58094 obs. of  9 variables:
 $ state      : chr  "Alabama" "Alabama" "Alabama" "Alabama" ...
 $ date       : chr  "2023-01-04" "2020-04-25" "2023-02-26" "2022-12-03" ...
 $ fips       : int  1 1 1 1 1 1 1 1 1 1 ...
 $ cases      : int  1587224 6213 1638348 1549285 8691 524367 1321892 1088370 1153149 814025 ...
 $ deaths     : int  21263 213 21400 21129 343 10807 19676 16756 16826 15179 ...
 $ geo_id     : int  1 1 1 1 1 1 1 1 1 1 ...
 $ population : int  4887871 4887871 4887871 4887871 4887871 4887871 4887871 4887871 4887871 4887871 ...
 $ pop_density: num  96.5 96.5 96.5 96.5 96.5 ...
 $ abb        : chr  "AL" "AL" "AL" "AL" ...

3. Format the data

  • Make date into a date variable

  • Make state into a factor variable

  • Order the data first by state, second by date

  • Confirm the variables are now correctly formatted

  • Inspect the range values for each variable

    # format the date
cv_states$date <- as.Date(cv_states$date, format="%Y-%m-%d")

# format the state and state abbreviation (abb) variables
state_list <- unique(cv_states$state)
cv_states$state <- factor(cv_states$state, levels = state_list)
abb_list <- unique(cv_states$abb)
cv_states$abb <- factor(cv_states$abb, levels = abb_list)

### FINISH THE CODE HERE 
# order the data first by state, second by date
cv_states <- cv_states[order(cv_states$state,cv_states$date),]

# Confirm the variables are now correctly formatted
str(cv_states)
    'data.frame':   58094 obs. of  9 variables:
 $ state      : Factor w/ 52 levels "Alabama","Alaska",..: 1 1 1 1 1 1 1 1 1 1 ...
 $ date       : Date, format: "2020-03-13" "2020-03-14" ...
 $ fips       : int  1 1 1 1 1 1 1 1 1 1 ...
 $ cases      : int  6 12 23 29 39 51 78 106 131 157 ...
 $ deaths     : int  0 0 0 0 0 0 0 0 0 0 ...
 $ geo_id     : int  1 1 1 1 1 1 1 1 1 1 ...
 $ population : int  4887871 4887871 4887871 4887871 4887871 4887871 4887871 4887871 4887871 4887871 ...
 $ pop_density: num  96.5 96.5 96.5 96.5 96.5 ...
 $ abb        : Factor w/ 52 levels "AL","AK","AZ",..: 1 1 1 1 1 1 1 1 1 1 ...
    head(cv_states)
           state       date fips cases deaths geo_id population pop_density abb
1029 Alabama 2020-03-13    1     6      0      1    4887871    96.50939  AL
597  Alabama 2020-03-14    1    12      0      1    4887871    96.50939  AL
282  Alabama 2020-03-15    1    23      0      1    4887871    96.50939  AL
12   Alabama 2020-03-16    1    29      0      1    4887871    96.50939  AL
266  Alabama 2020-03-17    1    39      0      1    4887871    96.50939  AL
78   Alabama 2020-03-18    1    51      0      1    4887871    96.50939  AL
    tail(cv_states)
            state       date fips  cases deaths geo_id population pop_density abb
57902 Wyoming 2023-03-18   56 185640   2009     56     577737    5.950611  WY
57916 Wyoming 2023-03-19   56 185640   2009     56     577737    5.950611  WY
57647 Wyoming 2023-03-20   56 185640   2009     56     577737    5.950611  WY
57867 Wyoming 2023-03-21   56 185800   2014     56     577737    5.950611  WY
58057 Wyoming 2023-03-22   56 185800   2014     56     577737    5.950611  WY
57812 Wyoming 2023-03-23   56 185800   2014     56     577737    5.950611  WY
    # Inspect the range values for each variable.
head(cv_states)
           state       date fips cases deaths geo_id population pop_density abb
1029 Alabama 2020-03-13    1     6      0      1    4887871    96.50939  AL
597  Alabama 2020-03-14    1    12      0      1    4887871    96.50939  AL
282  Alabama 2020-03-15    1    23      0      1    4887871    96.50939  AL
12   Alabama 2020-03-16    1    29      0      1    4887871    96.50939  AL
266  Alabama 2020-03-17    1    39      0      1    4887871    96.50939  AL
78   Alabama 2020-03-18    1    51      0      1    4887871    96.50939  AL
    summary(cv_states)
               state            date                 fips           cases         
 Washington   : 1158   Min.   :2020-01-21   Min.   : 1.00   Min.   :       1  
 Illinois     : 1155   1st Qu.:2020-12-06   1st Qu.:16.00   1st Qu.:  112125  
 California   : 1154   Median :2021-09-11   Median :29.00   Median :  418120  
 Arizona      : 1153   Mean   :2021-09-10   Mean   :29.78   Mean   :  947941  
 Massachusetts: 1147   3rd Qu.:2022-06-17   3rd Qu.:44.00   3rd Qu.: 1134318  
 Wisconsin    : 1143   Max.   :2023-03-23   Max.   :72.00   Max.   :12169158  
 (Other)      :51184                                                          
     deaths           geo_id        population        pop_density       
 Min.   :     0   Min.   : 1.00   Min.   :  577737   Min.   :    1.292  
 1st Qu.:  1598   1st Qu.:16.00   1st Qu.: 1805832   1st Qu.:   43.659  
 Median :  5901   Median :29.00   Median : 4468402   Median :  107.860  
 Mean   : 12553   Mean   :29.78   Mean   : 6397965   Mean   :  423.031  
 3rd Qu.: 15952   3rd Qu.:44.00   3rd Qu.: 7535591   3rd Qu.:  229.511  
 Max.   :104277   Max.   :72.00   Max.   :39557045   Max.   :11490.120  
                                                     NA's   :1106       
      abb       
 WA     : 1158  
 IL     : 1155  
 CA     : 1154  
 AZ     : 1153  
 MA     : 1147  
 WI     : 1143  
 (Other):51184  
    min(cv_states$date)
    [1] "2020-01-21"
    max(cv_states$date)
    [1] "2023-03-23"

4. Add new_cases and new_deaths and correct outliers

  • Add variables for new cases, new_cases, and new deaths, new_deaths:

    • Hint: You can set new_cases equal to the difference between cases on date i and date i-1, starting on date i=2

  • Filter to dates after June 1, 2021

  • Use plotly for EDA: See if there are outliers or values that don’t make sense for new_cases and new_deaths. Which states and which dates have strange values?

  • Correct outliers: Set negative values for new_cases or new_deaths to 0

  • Recalculate cases and deaths as cumulative sum of updated new_cases and new_deaths

  • Get the rolling average of new cases and new deaths to smooth over time

  • Inspect data again interactively

    # Add variables for new_cases and new_deaths:
for (i in 1:length(state_list)) {
  cv_subset <- subset(cv_states, state == state_list[i])
  cv_subset <- cv_subset[order(cv_subset$date),]

  # add starting level for new cases and deaths
  cv_subset$new_cases <- cv_subset$cases[1]
  cv_subset$new_deaths <- cv_subset$deaths[1]

  ### FINISH THE CODE HERE
  for (j in 2:nrow(cv_subset)) {
    cv_subset$new_cases[j] <- cv_subset$cases[j] - cv_subset$cases[j-1]
    cv_subset$new_deaths[j] <- cv_subset$deaths[j] - cv_subset$deaths[j-1]
  }

  # include in main dataset
  cv_states$new_cases[cv_states$state==state_list[i]] <- cv_subset$new_cases
  cv_states$new_deaths[cv_states$state==state_list[i]] <- cv_subset$new_deaths
}

# Focus on recent dates
cv_states <- cv_states |> dplyr::filter(date >= "2021-06-01")

#install.packages("plotly")
install.packages("zoo")
    The following package(s) will be installed:
- zoo [1.8-14]
These packages will be installed into "~/Desktop/PM566labs/renv/library/macos/R-4.4/aarch64-apple-darwin20".

# Installing packages --------------------------------------------------------
- Installing zoo ...                            OK [linked from cache]
Successfully installed 1 package in 7.9 milliseconds.
    library(plotly)
library(ggplot2)
library(zoo)
# Inspect outliers in new_cases using plotly
p1 <- ggplot(cv_states, aes(x = date, y = new_cases, color = state)) + geom_point(size = .5, alpha = 0.5)
ggplotly(p1)
    p2 <- ggplot(cv_states, aes(x = date, y = new_deaths, color = state)) + geom_point(size = .5, alpha = 0.5)
ggplotly(p2)
    # set negative new case or death counts to 0
cv_states$new_cases[cv_states$new_cases<0] <- 0
cv_states$new_deaths[cv_states$new_deaths<0] <- 0

# Re-calculate `cases` and `deaths` as cumulative sum of updated `new_cases` and `new_deaths`
for (i in 1:length(state_list)) {
  cv_subset = subset(cv_states, state == state_list[i])

  # add starting level for new cases and deaths
  cv_subset$cases <- cv_subset$cases[1]
  cv_subset$deaths <- cv_subset$deaths[1]

  ### FINISH CODE HERE
  for (j in 2:nrow(cv_subset)) {
    cv_subset$cases[j] <- cv_subset$new_cases[j] + cv_subset$cases[j-1]
    cv_subset$deaths[j] <- cv_subset$new_deaths[j] + cv_subset$deaths[j-1]
  }
  # include in main dataset
  cv_states$cases[cv_states$state==state_list[i]] <- cv_subset$cases
  cv_states$deaths[cv_states$state==state_list[i]] <- cv_subset$deaths
}

# Smooth new counts
cv_states$new_cases <- zoo::rollmean(cv_states$new_cases, k=7, fill=NA, align='right') |> round(digits = 0)
cv_states$new_deaths <- zoo::rollmean(cv_states$new_deaths, k=7, fill=NA, align='right') |> round(digits = 0)

# Inspect data again interactively
p2 <- ggplot(cv_states, aes(x = date, y = new_deaths, color = state)) + geom_line() + geom_point(size = .5, alpha = 0.5)
ggplotly(p2)

5. Add additional variables

  • Add population-normalized (by 100,000) variables for each variable type (rounded to 1 decimal place). Make sure the variables you calculate are in the correct format (numeric). You can use the following variable names:

    • per100k = cases per 100,000 population

    • newper100k= new cases per 100,000

    • deathsper100k = deaths per 100,000

    • newdeathsper100k = new deaths per 100,000

  • Add a naive CFR variable representing deaths / cases on each date for each state

  • Create a data frame representing values on the most recent date, cv_states_today, as done in lecture

    ### FINISH CODE HERE
# add population normalized (by 100,000) counts for each variable
cv_states$per100k <- as.numeric(format(round(cv_states$cases/(cv_states$population/100000),1),nsmall=1))
cv_states$newper100k <- as.numeric(format(round(cv_states$new_cases/(cv_states$population/100000),1),nsmall=1))
cv_states$deathsper100k <- as.numeric(format(round(cv_states$deaths/(cv_states$population/100000),1),nsmall=1))
cv_states$newdeathsper100k <- as.numeric(format(round(cv_states$new_deaths/(cv_states$population/100000),1),nsmall=1))

# add a naive_CFR variable = deaths / cases
cv_states <- cv_states |> mutate(naive_CFR = round((deaths*100/cases),2))

# create a `cv_states_today` variable
cv_states_today <- subset(cv_states, date==max(cv_states$date))

II. Scatterplots

6. Explore scatterplots using plot_ly()

  • Create a scatterplot using plot_ly() representing pop_density vs. various variables (e.g. cases, per100k, deaths, deathsper100k) for each state on most recent date (cv_states_today)

    • Color points by state and size points by state population

    • Use hover to identify any outliers.

    • Remove those outliers and replot.

  • Choose one plot. For this plot:

    • Add hoverinfo specifying the state name, cases per 100k, and deaths per 100k, similarly to how we did this in the lecture notes

    • Add layout information to title the chart and the axes

    • Enable hovermode = "compare"

      cv_states_today |> 
  plot_ly(x = ~pop_density, y = ~cases, 
          type = "scatter", mode = 'markers', color = ~state,
          size = ~population, sizes = c(5, 70), marker = list(sizemode='diameter', opacity=0.5))
      # filter out "District of Columbia"
cv_states_today_filter <- cv_states_today |> filter(state!="District of Columbia")

# pop_density vs. cases after filtering
cv_states_today_filter |> 
  plot_ly(x = ~pop_density, y = ~cases, 
          type = "scatter", mode = 'markers', color = ~state,
          size = ~population, sizes = c(5, 70), marker = list(sizemode='diameter', opacity=0.5))
      # pop_density vs. deathsper100k
cv_states_today_filter |> 
  plot_ly(x = ~pop_density, y = ~deathsper100k,
          type = "scatter", mode = 'markers', color = ~state,
          size = ~population, sizes = c(5, 70), marker = list(sizemode='diameter', opacity=0.5))
      # Adding hoverinfo
cv_states_today_filter |> 
  plot_ly(x = ~pop_density, y = ~deathsper100k,
          type ="scatter", mode = 'markers', color = ~state,
          size = ~population, sizes = c(5, 70), marker = list(sizemode='diameter', opacity=0.5),
          hoverinfo = 'text',
          text = ~paste( paste(state, ":", sep=""), paste(" Cases per 100k: ", per100k, sep="") , 
                         paste(" Deaths per 100k: ", deathsper100k, sep=""), sep = "<br>")) |>
  layout(title = "Population-normalized COVID-19 deaths (per 100k) vs. population density for US states",
                  yaxis = list(title = "Deaths per 100k"), xaxis = list(title = "Population Density"),
         hovermode = "compare")

7. Explore scatterplot trend interactively using ggplotly() and geom_smooth()

  • For pop_density vs. newdeathsper100k create a chart with the same variables using gglot_ly()

  • Explore the pattern between  and  using geom_smooth()

    ### FINISH CODE HERE
p <- ggplot(cv_states_today_filter, aes(x=pop_density, y=deathsper100k, size=population)) + geom_point() + geom_smooth()
ggplotly(p)

8. Multiple line chart

  • Create a line chart of the naive_CFR for all states over time using plot_ly()

    • Use the zoom and pan tools to inspect the naive_CFR for the states that had an increase in September.

  • Create one more line chart, for Florida only, which shows new_cases and new_deaths together in one plot. Hint: look for an add_*()

    • Use hoverinfo to “eyeball” the approximate peak of deaths and peak of cases. What is the time delay between the peak of cases and the peak of deaths?

      ### FINISH CODE HERE
# Line chart for naive_CFR for all states over time using `plot_ly()`
plot_ly(cv_states, x = ~date, y = ~naive_CFR, color = ~state, type = "scatter", mode = "lines")
      ### FINISH CODE HERE
# Line chart for Florida showing new_cases and new_deaths together (two lines)
cv_states |> filter(state=="Florida") |> 
  plot_ly(x = ~date, y = ~new_cases, type = "scatter", mode = "lines") |> 
  add_lines(x = ~date, y = ~new_deaths, type = "scatter", mode = "lines")

9. Heatmaps

Create a heatmap to visualize new_cases for each state on each date greater than June 1st, 2021 - Start by mapping selected features in the dataframe into a matrix using the tidyr package function pivot_wider(), naming the rows and columns, as done in the lecture notes - Use plot_ly() to create a heatmap out of this matrix. Which states stand out? - Repeat with newper100k variable. Now which states stand out? - Create a second heatmap in which the pattern of new_cases for each state over time becomes more clear by filtering to only look at dates every two weeks

### FINISH CODE HERE
# Map state, date, and new_cases to a matrix
library(tidyr)
cv_states_mat <- cv_states |> select(state, date, new_cases) |> dplyr::filter(date>as.Date("2021-06-15"))
cv_states_mat2 <- as.data.frame(pivot_wider(cv_states_mat, names_from = state, values_from = new_cases))
rownames(cv_states_mat2) <- cv_states_mat2$date
cv_states_mat2$date <- NULL
cv_states_mat2 <- as.matrix(cv_states_mat2)

# Create a heatmap using plot_ly()
plot_ly(x=colnames(cv_states_mat2), y=rownames(cv_states_mat2),
             z=~cv_states_mat2,
             type="heatmap",
             showscale=T)
# Repeat with newper100k
cv_states_mat <- cv_states |> select(state, date, newper100k) |> dplyr::filter(date>as.Date("2021-06-15"))
cv_states_mat2 <- as.data.frame(pivot_wider(cv_states_mat, names_from = state, values_from = newper100k))
rownames(cv_states_mat2) <- cv_states_mat2$date
cv_states_mat2$date <- NULL
cv_states_mat2 <- as.matrix(cv_states_mat2)

plot_ly(x=colnames(cv_states_mat2), y=rownames(cv_states_mat2),
             z=~cv_states_mat2,
             type="heatmap",
             showscale=T)
# Create a second heatmap after filtering to only include dates every other week
filter_dates <- seq(as.Date("2021-06-15"), as.Date("2021-11-01"), by="2 weeks")

cv_states_mat <- cv_states |> select(state, date, newper100k) |> filter(date %in% filter_dates)
cv_states_mat2 <- as.data.frame(pivot_wider(cv_states_mat, names_from = state, values_from = newper100k))
rownames(cv_states_mat2) <- cv_states_mat2$date
cv_states_mat2$date <- NULL
cv_states_mat2 <- as.matrix(cv_states_mat2)

# Create a heatmap using plot_ly()
plot_ly(x=colnames(cv_states_mat2), y=rownames(cv_states_mat2),
             z=~cv_states_mat2,
             type="heatmap",
             showscale=TRUE)

10. Map

  • Create a map to visualize the naive_CFR by state on October 15, 2021

  • Compare with a map visualizing the naive_CFR by state on most recent date

  • Plot the two maps together using subplot(). Make sure the shading is for the same range of values (google is your friend for this)

  • Describe the difference in the pattern of the CFR.

    ### For specified date

pick.date <- "2021-10-15"

# Extract the data for each state by its abbreviation
cv_per100 <- cv_states |> filter(date==pick.date) |> select(state, abb, newper100k, cases, deaths) # select data
cv_per100$state_name <- cv_per100$state
cv_per100$state <- cv_per100$abb
cv_per100$abb <- NULL

# Create hover text
cv_per100$hover <- with(cv_per100, paste(state_name, '<br>', "Cases per 100k: ", newper100k, '<br>', "Cases: ", cases, '<br>', "Deaths: ", deaths))

# Set up mapping details
set_map_details <- list(
  scope = 'usa',
  projection = list(type = 'albers usa'),
  showlakes = TRUE,
  lakecolor = toRGB('white')
)

# Make sure both maps are on the same color scale
shadeLimit <- 125

# Create the map
fig <- plot_geo(cv_per100, locationmode = 'USA-states') |> 
  add_trace(
    z = ~newper100k, text = ~hover, locations = ~state,
    color = ~newper100k, colors = 'Purples'
  )
fig <- fig |> colorbar(title = paste0("Cases per 100k: ", pick.date), limits = c(0,shadeLimit))
fig <- fig |> layout(
    title = paste('Cases per 100k by State as of ', pick.date, '<br>(Hover for value)'),
    geo = set_map_details
  )
fig_pick.date <- fig

#############
### Map for today's date

# Extract the data for each state by its abbreviation
cv_per100 <- cv_states_today |>  select(state, abb, newper100k, cases, deaths) # select data
cv_per100$state_name <- cv_per100$state
cv_per100$state <- cv_per100$abb
cv_per100$abb <- NULL

# Create hover text
cv_per100$hover <- with(cv_per100, paste(state_name, '<br>', "Cases per 100k: ", newper100k, '<br>', "Cases: ", cases, '<br>', "Deaths: ", deaths))

# Set up mapping details
set_map_details <- list(
  scope = 'usa',
  projection = list(type = 'albers usa'),
  showlakes = TRUE,
  lakecolor = toRGB('white')
)

# Create the map
fig <- plot_geo(cv_per100, locationmode = 'USA-states') |> 
  add_trace(
    z = ~newper100k, text = ~hover, locations = ~state,
    color = ~newper100k, colors = 'Purples'
  )
fig <- fig |> colorbar(title = paste0("Cases per 100k: ", Sys.Date()), limits = c(0,shadeLimit))
fig <- fig |> layout(
    title = paste('Cases per 100k by State as of', Sys.Date(), '<br>(Hover for value)'),
    geo = set_map_details
  )
fig_Today <- fig


### Plot together 
subplot(fig_pick.date, fig_Today, nrows = 2, margin = .05)

The early-pandemic CFR map shows strong regional differences, with several northern and western states experiencing disproportionately high fatality ratios. By the most recent date, CFR becomes much lower and far more uniform across states, reflecting improvements in treatment, testing, and population immunity.